GeoRocket: A scalable and cloud-based data store for big geospatial files

One of the projects I’m working on at Fraunhofer IGD is GeoRocket, a high-performance data store for geospatial files. It’s my pleasure to announce that my research paper about this application has just been published in SoftwareX, an open-access journal by Elsevier.

GeoRocket has been designed to manage very large geospatial datasets in the cloud. It employs a novel way to handle arbitrarily large datasets by splitting them into chunks that are processed individually.

The software has a modern reactive architecture and makes use of existing services including Elasticsearch and storage back ends such as MongoDB or Amazon S3. GeoRocket is schema agnostic and supports a wide range of heterogeneous geospatial file formats. It is also format preserving and does not alter imported data in any way.

The main benefits of GeoRocket are its performance, scalability, and usability, which make it suitable for a number of scientific and commercial use cases dealing with very high data volumes, complex datasets, and high velocity (Big Data). GeoRocket also provides many opportunities for further research in the area of geospatial data management.


Krämer, M. (2020). GeoRocket: A scalable and cloud-based data store for big geospatial files. SoftwareX, 11.
[ | PDF ]


The paper has been published under the CC-BY 4.0 license. You may download the final manuscript here.

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Posted by Michel Krämer
on 7 February 2020

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